CN103957505A - Behavior trace detection analysis and service providing system and method based APs - Google Patents

Behavior trace detection analysis and service providing system and method based APs Download PDF

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CN103957505A
CN103957505A CN201410162989.6A CN201410162989A CN103957505A CN 103957505 A CN103957505 A CN 103957505A CN 201410162989 A CN201410162989 A CN 201410162989A CN 103957505 A CN103957505 A CN 103957505A
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rssi
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CN103957505B (en
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陈光旭
王强
诸彤宇
李明扬
李文博
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Beihang University
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Abstract

The invention discloses a behavior trace detection analysis and service providing system and method based on APs. The system comprises a signal collection module, a data signal receiving interface, a positioning module, a positioning data storage module, a behavior trace analysis module and a service providing module, wherein the signal collection module is composed of multiple APs with the signal collection ability. The method includes the steps that (1) the APs collect wireless signal intensity between different terminals and MAC addresses of the terminals; (2) the mobile terminals are positioned; (3) position traces of the mobile terminals are analyzed; (4) corresponding services are provided according to service requests put forward by the mobile terminals and other devices. By means of the system and method, the outdoor and indoor mobile terminals can be positioned precisely in time, modification is carried out according to different mobile terminal attributes, and positioning precision is increased. Platform limitations do not exist, positioning and position data analysis can be carried out on mobile phones, notebook computers, tablet computers and other mobile terminals, and accordingly corresponding services are provided for terminal users and other users.

Description

A kind of analysis of behavior track detection and service provider system and method based on AP
Technical field
The present invention relates to a kind of location, position analysis system and implementation method, particularly relate to location, position and behavioural analysis system and the implementation method of a kind of AP based on Wi-Fi technology.
Background technology
Along with the quick increase of data service and multimedia service, people increase day by day to location and the demand of navigation, especially in complicated indoor environment, in the environment such as airport hall, exhibition room, warehouse, supermarket, library, underground parking, mine, usually need to determine that mobile terminal or its holder, facility and article are in indoor positional information.In recent years, along with mobile device technology is fast-developing and day by day universal, the mobility that how to make full use of equipment has caused numerous researchers' concern for user provides more abundant and perfect service, and location-based service (Location Based Service, LBS) has become one of hot issue of mobile computing research field in recent years.The prerequisite that position-based service is provided is that mobile device need to be known self residing physical location.In the time that mobile device is positioned at open air, GPS (Global Position System) can provide a kind of simple and effective solution for this class application.But GPS cannot work in building, therefore how in building, mobile device being positioned is still the problem in science that needs solve.Along with popularizing of Wi-Fi technology application, WLAN (Wireless Local Area Network) access point (Access Point, AP) widespread deployment in the building of city, this such as makes, in a lot of indoor environments (office building, coffee shop etc.) almost every nook and cranny to be covered by Wi-Fi signal, thereby indoor positioning technology based on Wi-Fi signal has obtained development rapidly.We just can utilize mobile device and access point (Access Point, AP) contained reception information strength (RSSI) information positions calculating or in the beacon signal that regularly sends of base station, and can analyze according to positional information the behavior track of mobile terminal, and then provide difference that service is provided according to the request of sending for distinct device.
Use at present the wider localization method based on Wi-Fi signal to mainly contain and arrive angle positioning mode (Angle0f Arrival, AOA), the positioning mode time of advent (Time Of Arrival, TOA), signal strength analysis method and location fingerprint classification etc.Due to indoor environment complexity, it is very accurate that the measurement of wireless signal incident angle is also difficult to.Therefore, the method based on AOA or TOA is not very applicable to indoor environment.And in the interior space, exist a large amount of barriers, as wall, door, tables and chairs, bin etc., can produce the impact that is difficult to calculating to the attenuation degree of signal when time on their propagation paths in wireless signal, therefore signal strength analysis method utilizes the range accuracy of RSSI value not high under indoor environment.Location fingerprint classification is the localization method of a class based on machine learning, its basic thought is the received signals fingerprint (the RSSI value of AP) that records ad-hoc location, by the similarity degree of compare test sample and location fingerprint, and then the physical location of sample estimates.Compare with signal strength analysis method with AOA, TOA, location fingerprint classification based on RSSI does not rely on angle or the such geometric sense of distance, but foundation using the measured value of RSSI itself as calibration position, so just evade the problems such as received signal strength range finding, had better positioning precision.There is location accurately, position-based service just can be provided.And revise according to existing behavior track, further improved the precision of location.
Summary of the invention
The object of the present invention is to provide a kind of behavior track detection based on AP to analyze and service provider system system and implementation method.By the analyzing and processing of locating module, the wireless signal with between each mobile terminal that each WAP (wireless access point) (AP) can be collected mates with fingerprint base and then the particular location at definite mobile terminal place.And then continual mobile terminal is recorded in to locator data storage module in the positional information of different time.Behavior trajectory analysis module can be analyzed according to the position data of mobile terminal that is recorded in locator data storage module track and the behavior of mobile terminal.The request that service providing module can be sent according to different user, provides corresponding service.
The analysis of behavior track detection and the service provider system that the present invention is based on AP for solving above proposed technical problem, comprising:
Signal acquisition module, formed at the AP with signal collection ability of diverse location by multiple, for obtaining the wireless signal strength (RSSI) of different mobile terminal user transmission and MAC (Media Access Control, the medium access control) address information of different mobile terminal.Wherein, mobile terminal comprises: mobile phone, notebook computer, panel computer, palm equipment for surfing the net, POS (Point of sales) machine, vehicle-mounted computer, wearable device;
Data-signal receiving interface, for receiving the wireless signal strength (RSSI) of the different mobile terminal user transmission gathering from signal acquisition module and MAC (Media Access Control, the medium access control) address information of different mobile terminal;
Locating module, the data of utilizing data-signal receiving interface to obtain, utilize band of position to have Fingerprint Classification Using that weights distribute and relevant behavior trace information, determine the particular location of different mobile terminal in space, and by equipment and cached location information in locator data storage module.Wherein location fingerprint classification, comprising: training stage, positioning stage and data correction stage.Wherein set up position-wireless signal fingerprint recognition database in the training stage, and calculate the weights of different AP, for positioning stage;
Locator data storage module, the different time producing for recording all locating modules, the positional information of different mobile terminal;
Behavior trajectory analysis module, for analyzing track and the behavior of different mobile terminal (user), and by information storage;
Service providing module, according to different service requests, based on the data of locator data storage module and behavior trajectory analysis module, for user provides respective service.
In addition, the present invention also provides a kind of data training method, comprising:
(1) data collection phase: collect the wireless signal strength of the measured terminal of different AP on each datum mark, as training data;
(2) the data processing stage: calculate the characteristic value of the wireless signal strength of the measured terminal of different AP on each datum mark, and calculate the weights distribution of each AP;
(3) the data correction stage: in actual location as discovering and location precision deficiency, or existing measurement data goes wrong, existing training data be can add or revise, and the characteristic value of the wireless signal strength on each datum mark and the distribution of the weights of each AP recalculated according to new training data.
The present invention also provides a kind of implementation method of the indoor positioning based on AP, comprising:
(1) wireless signal strength between collection and the different terminals of signal acquisition module AP and the MAC Address of terminal;
(2) data-signal receiving interface receives the wireless signal strength (RSSI) of the different mobile terminal user transmission gathering from signal acquisition module and MAC (Media Access Control, the medium access control) address information of different mobile terminal;
(3) data that locating module obtains according to data-signal receiving interface, utilize band of position to have the Fingerprint Classification Using of weights distribution and relevant behavior trace information, determine the particular location of different mobile terminal in space.And by equipment and cached location information in locator data storage module, use for other modules.
The present invention utilizes Wi-Fi technology, and utilizes long-range locating module and the behavior trajectory analysis module with higher operational capability, can position timely and effectively, and calculate the running orbit of equipment.By service providing module, can be timely and effectively for various users provide different services, as timely positioning service, navigation Service, space passenger flow analysing and investigation service, geographical fence service, position-based advertisement pushing function, location-based payment services and payment verification service etc.
Brief description of the drawings
Fig. 1 is that the behavior track detection that the present invention is based on AP is analyzed and service provider system frame diagram;
Fig. 2 is the schematic diagram of layout reference point.
Embodiment
Behavior track detection based on AP of the present invention is analyzed and service provider system, comprising:
Signal acquisition module, formed by multiple AP with signal collection ability, for obtaining the wireless signal strength (RSSI) of different mobile terminal user transmission and MAC (Media Access Control, the medium access control) address information of different mobile terminal.
Data-signal receiving interface, for receiving the wireless signal strength (RSSI) of the different mobile terminal user transmission gathering from signal acquisition module and MAC (Media Access Control, the medium access control) address information of different mobile terminal;
Locating module, the data of utilizing data-signal receiving interface to obtain, utilize band of position to have Fingerprint Classification Using that weights distribute and relevant behavior trace information, determine the particular location of different mobile terminal in space, and by equipment and cached location information in locator data storage module;
Locator data storage module, the different time producing for recording all locating modules, the positional information of different mobile terminal;
Behavior trajectory analysis module, for analyzing track and the behavior of different mobile terminal (user), and by information storage;
Service providing module, according to different service requests, based on the data of locator data storage module and behavior trajectory analysis module, for user provides respective service.
Analyze and service provider system for the above-mentioned behavior track detection based on AP, the method for its concrete realization is shown in Fig. 1, comprising:
Pretreatment stage, the training stage of executing location fingerprint technique, its concrete implementation is as follows:
(1) data collection phase: collect the wireless signal strength of the measured terminal of different AP on each datum mark, as training data;
(2) the data processing stage: (layout of datum mark can be as shown in Figure 2 at each reference point location as this terminal for the mean value of the wireless signal strength using the measured terminal of different AP of a period of time on each datum mark or mode, in space, equidistantly determine) to this AP characteristic value, determine the scope of characteristic value, and characteristic value based on having calculated, the weights that calculate each AP distribute, and method is as follows:
If can detect in test zone that the set of whole AP of equipment is V, sample a will use sample attribute vector s a=(s a1, s a2..., s an) wherein each component s is described aithe value (i.e. the characteristic value of the RSSI of i AP) that represents i attribute in this sample, n is the quantity of whole AP.If the value of specific AP do not detected in a sample, its RSSI value is made as to-129, low by 1 than the lower limit of RSSI.
Before giving each AP distribution weights, first need to calculate the coefficient correlation between all AP that can detect in test zone.Remember that between two AP, coefficient correlation is r ij, its computing formula is suc as formula (1), wherein i, j ∈ V, Rssi iand Rssi jrepresent respectively the measured value of the RSSI of i, a j AP, E (a) and D (a) represent respectively mathematic expectaion and the variance of stochastic variable a, and Cov (a, b) represents the covariance of stochastic variable a and b.All coefficient correlation can form correlation matrix R.The computational process of correlation matrix is used whole training datas, does not distinguish the affiliated class of training sample, so the coefficient correlation finally drawing is the coefficient correlation of every couple of AP in whole test zones.In addition, while calculating the coefficient correlation of a pair of AP, only consider the data that these two AP can be detected simultaneously.
r ij = Cov ( Rssi i , Rssi j ) D ( Rssi i ) · D ( Rssi j ) = E ( Rssi i · Rssi j ) - E ( Rssi i ) · E ( Rssi j ) E ( Rssi i 2 ) - E ( Rssi i ) 2 · E ( Rssi j 2 ) - E ( Rssi j ) 2 - - - ( 1 )
Can be found out by formula (1), calculating when correlation matrix, may run into following situation and make coefficient correlation without definition: 1) can detection range not occuring simultaneously of two AP, they were never detected simultaneously; 2) wherein the variance of the measured value of the RSSI of at least one AP is 0.Calculate for convenient, in the time occurring coefficient correlation without definition, defining the coefficient correlation that this AP is right is 0.In addition, right definition r ii=1.
After obtaining correlation matrix, just can calculate with formula (2) weights of each AP:
w i = 1 R i · R i T , i ∈ V - - - ( 2 )
In formula (2), w ithe weights of distributing to i AP, R i=(r i1, r i2..., r in) be i row vector in correlation matrix R.In simple terms, the weights of each AP are inverses of the quadratic sum of the coefficient correlation of AP in it and all V (comprise it oneself), so higher its weights of the correlation (absolute value of coefficient correlation) of all AP that can detect in AP and test zone are lower.This is because the correlation of this AP and other AP is higher, just represents that the information that information that it provides and other AP provide has more repetition; Otherwise an AP and other AP are more independent, and its weights are larger, and right there is w i∈ (0,1].
For the ease of the feature of analyzing and training data, the dimension D that the weights sum that defines all AP is training dataset, suc as formula (3).
D＝W·N T,N＝(1,1,…,1) (3)
Wherein W=(w 1, w 2..., w n) be the weight vector of training dataset, N is that n dimension row vector and important value are all 1.Obviously between the concentrated AP comprising of a training data, correlation is higher, and the dimension of this training dataset is less; Otherwise the dimension of training dataset is larger, and training dataset is arbitrarily had to dimension D ∈ [1, n].
(3) the data correction stage: in actual location as discovering and location precision deficiency, or existing measurement data goes wrong, existing training data be can add or revise, and the characteristic value of the wireless signal strength on each datum mark and the distribution of the weights of each AP recalculated according to new training data.
First stage, signal acquisition module is obtained the current wireless signal strength (RSSI) that different AP are transmitted of different mobile terminal user and MAC (Media Access Control, the medium access control) address information of different mobile terminal;
Second stage, data-signal receiving interface receives the wireless signal strength (RSSI) of the different mobile terminal user transmission gathering from signal acquisition module and MAC (Media Access Control, the medium access control) address information of different mobile terminal;
Phase III is actual positioning stage, the data that locating module utilizes data-signal receiving interface to obtain, utilize band of position to have the Fingerprint Classification Using of weights distribution and relevant behavior trace information, determine the particular location of different mobile terminal in space, and by equipment and cached location information in locator data storage module, detailed process is as follows:
1) locating module utilizes the data that data-signal receiving interface obtains;
2) data are classified according to the MAC Address of terminal;
3) if the quantity of the AP return value that a certain terminal MAC obtains is at the same time less than 3, ignore this terminal;
4) if the scope that exceedes datum mark characteristic value of the AP return value that a certain terminal MAC obtains is at the same time ignored this terminal;
5) according to the weights of each AP, if the signal strength signal intensity that the different AP of this sample point a obtain is respectively [s a, 1, s a, 2..., s a, n], each characteristic value of datum mark b is [s b, 1, s b, 2..., s b, n], can use simply formula (4) to replace Euclidean distance to calculate the Weighted distance of this sample point and each datum mark.
d ( a , b ) = Σ i ∈ V w i ( s a , i - s b , i ) 2 - - - ( 4 )
Wherein, d (a, b) represents the distance of the weighting between a, two points of b;
6) Weighted distance of sample point and each datum mark is sorted from small to large;
7) select and one of current sample point Weighted distance minimum or the minimum several datum marks basis as computing terminal position;
8) bonding behavior trace information is determined terminal location;
9) by position data and terminal MAC data after calculating, time data is recorded to locator data storage module;
10) behavior trajectory analysis module obtains data from locator data storage module, analyzes track and the behavior of different mobile terminal (user), and by information storage;
11) service providing module receives service request, according to different service requests, based on the data of locator data storage module and behavior trajectory analysis module, for user provides respective service.
Therefore, the analysis of behavior track detection and service provider system based on AP of the present invention is MAC Address, the wireless signal strength information of a mobile terminal that is sent to navigation system gathering according to AP, use position-based fingerprint identification location method to calculate mobile terminal user position information, and obtain the judgement of track and behavior by calculating mobile terminal, the system of respective service is provided according to the service request of sending.

Claims (7)

1. the behavior track detection based on AP is analyzed and a service provider system, and described AP refers to WAP (wireless access point), it is characterized in that, comprising:
Signal acquisition module, comprises multiple WLAN access point Access Point with signal collection ability at diverse location, i.e. AP, for obtaining the wireless signal strength RSSI of different mobile terminal user transmission and the mac address information of different mobile terminal;
Data-signal receiving interface, for receiving the wireless signal strength of the different mobile terminal user transmission gathering from signal acquisition module and the mac address information of different mobile terminal;
Locating module, the data of utilizing data-signal receiving interface to obtain, utilize band of position to have that distribute and the relevant behavior trace information of weights, determine the particular location of different mobile terminal in space, and by equipment and cached location information in locator data storage module;
Locator data storage module, the different time producing for recording all locating modules, the positional information of different mobile terminal;
Behavior trajectory analysis module, for analyzing track and the behavior of different mobile terminal, and by information storage, and can respond the request of locating module and provide data to it;
Service providing module, according to different service requests, based on the data of locator data storage module and behavior trajectory analysis module, for user provides respective service.
2. the behavior track detection based on AP as claimed in claim 1 is analyzed and service provider system, it is characterized in that, described mobile terminal comprises: mobile phone, notebook computer, panel computer, palm equipment for surfing the net, POS machine, vehicle-mounted computer, wearable device.
3. the behavior track detection based on AP as claimed in claim 1 is analyzed and service provider system, it is characterized in that, described available service comprises location-based service, comprise timely positioning service, navigation Service, space passenger flow analysing and investigation service, geographical fence service, position-based advertisement pushing function, location-based payment services and payment verification service.
4. the method that the behavior track detection analysis based on AP and service provide, comprises the system as claimed in claim 1, it is characterized in that the method utilized location fingerprint classification, and comprises the steps:
1) locating module utilizes the data that data-signal receiving interface obtains;
2) data are classified according to the MAC Address of terminal;
3) if the quantity of the AP return value that a certain terminal MAC obtains is at the same time less than 3, ignore this terminal;
4) if the scope that exceedes datum mark characteristic value of the AP return value that a certain terminal MAC obtains is at the same time ignored this terminal;
5) according to the weights of each AP, if the signal strength signal intensity that the different AP of this sample point a obtain is respectively [s a, 1, s a, 2..., s a, n], in space, each characteristic value of the datum mark b of known particular location coordinate is [s b, 1, s b, 2..., s b, n], described characteristic value is mean value or the mode of the terminal of the known fixing AP scanning survey wireless signal strength (RSSI) on known location point; Use formula (4) replaces Euclidean distance to calculate the Weighted distance of this sample point and each datum mark; The set that whole AP of equipment wherein can be detected in test zone is V, the numbering that i is AP, w ithe weights of distributing to i AP;
d ( a , b ) = Σ i ∈ V w i ( s a , i - s b , i ) 2 - - - ( 4 )
Wherein, d (a, b) represents the distance of the weighting between a, two points of b;
6) Weighted distance of sample point and each datum mark is sorted from small to large;
7) select and one of current sample point Weighted distance minimum or the minimum several datum marks basis as computing terminal position;
8) bonding behavior trace information is determined terminal location;
9) by position data and terminal MAC data after calculating, time data is recorded to locator data storage module;
10) behavior trajectory analysis module obtains data from locator data storage module, analyzes track and the behavior of different mobile terminal, and by information storage;
11) service providing module receives service request, according to different service requests, based on the data of locator data storage module and behavior trajectory analysis module, for user provides respective service.
5. method as claimed in claim 4, is characterized in that, in described locating module, location fingerprint classification, comprises the steps:
Training stage, positioning stage and data correction stage; Wherein, the training stage, target is to be to set up a location fingerprint Classification and Identification database, comprises characteristic value and the weights of each AP in calculating of equipment signal between each positioning datum point and each AP;
Positioning stage, the terminal obtaining by use goes to mate with the location fingerprint Classification and Identification database building before with the wireless signal strength between AP, by computing, thus the position of acquisition mobile phone users;
In the data correction stage, improve positioning precision, correction position fingerprint classification identification database.
6. method as claimed in claim 4, is characterized in that the described training stage comprises:
Data collection phase: collect the wireless signal strength of the measured terminal of different AP on each datum mark, as training data;
The data processing stage: calculate the characteristic value of the wireless signal strength of the measured terminal of different AP on each datum mark, and calculate the weights distribution of each AP;
The data correction stage: in actual location as discovering and location precision deficiency, or existing measurement data goes wrong, add or revise existing training data, and reenter the data processing stage according to new training data, calculate the characteristic value of the wireless signal strength on each datum mark and the weights of each AP and distribute.
7. method as claimed in claim 6, is characterized in that, the concrete grammar in described data processing stage, comprising:
The mean value of the wireless signal strength using the measured terminal of different AP of a period of time on each datum mark or mode as this terminal in this position to this AP characteristic value, determine the scope of characteristic value, and characteristic value based on having calculated, the weights that calculate each AP distribute, and method is as follows:
If can detect in test zone that the set of whole AP of equipment is V, sample a will use sample attribute vector s a=(s a1, s a2..., s an) wherein each component s is described airepresent the value of i attribute in this sample, i.e. the characteristic value of the RSSI of i AP, n is the quantity of whole AP; If specific AP do not detected in a sample, its RSSI value is made as to-129, low by 1 than the lower limit of RSSI;
Before giving each AP distribution weights, first need to calculate the coefficient correlation between all AP that can detect in test zone; Remember that between two AP, coefficient correlation is r ij, its computing formula is suc as formula (1), wherein i, j ∈ V, Rssi iand Rssi jrepresent respectively the measured value of the RSSI of i, a j AP, E (a) and D (a) represent respectively mathematic expectaion and the variance of stochastic variable a, and Cov (a, b) represents the covariance of stochastic variable a and b; All coefficient correlation can form correlation matrix R; The computational process of correlation matrix is used whole training datas, does not distinguish the affiliated class of training sample, so the coefficient correlation finally drawing is the coefficient correlation of every couple of AP in whole test zones; In addition, while calculating the coefficient correlation of a pair of AP, only consider the data that these two AP can be detected simultaneously; Use formula (1) is calculated the coefficient correlation of a pair of AP;
r ij = Cov ( Rssi i , Rssi j ) D ( Rssi i ) · D ( Rssi j ) = E ( Rssi i · Rssi j ) - E ( Rssi i ) · E ( Rssi j ) E ( Rssi i 2 ) - E ( Rssi i ) 2 · E ( Rssi j 2 ) - E ( Rssi j ) 2 - - - ( 1 )
By formula (1), calculating when correlation matrix, may run into following situation and make coefficient correlation without definition: 1) can detection range not occuring simultaneously of two AP, they were never detected simultaneously; 2) wherein the variance of the measured value of the RSSI of at least one AP is 0; In the time occurring coefficient correlation without definition, defining the coefficient correlation that this AP is right is 0; In addition, right definition r ii=1;
After obtaining correlation matrix, just calculate the weights of each AP by formula (2):
w i = 1 R i · R i T , i ∈ V - - - ( 2 )
In formula (2), w ithe weights of distributing to i AP, R i=(r i1, r i2..., r in) be i row vector in correlation matrix R; The weights of each AP are inverses of the quadratic sum of the coefficient correlation of the AP in it and all V, so higher its weights of the correlation of all AP that can detect in AP and test zone are lower; This is because the correlation of this AP and other AP is higher, just represents that the information that information that it provides and other AP provide has more repetition; Otherwise an AP and other AP are more independent, and its weights are larger, and right there is w i∈ (0,1];
The dimension D that the weights sum that defines all AP is training dataset, suc as formula (3)
D＝W·N T,N＝(1,1,…,1) (3)
Wherein W=(w 1, w 2..., w n) be the weight vector of training dataset, N is that n dimension row vector and important value are all 1; Between the concentrated AP comprising of a training data, correlation is higher, and the dimension of this training dataset is less; Otherwise the dimension of training dataset is larger, and training dataset is arbitrarily had to dimension D ∈ [1, n].
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